Distributionally Robust Variational Quantum Algorithms With Shifted Noise
Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is that quantum noise is highl...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10547365/ |
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| _version_ | 1832586843926822912 |
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| author | Zichang He Bo Peng Yuri Alexeev Zheng Zhang |
| author_facet | Zichang He Bo Peng Yuri Alexeev Zheng Zhang |
| author_sort | Zichang He |
| collection | DOAJ |
| description | Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is that quantum noise is highly unstable and thus it is likely to shift in real time. This presents a critical problem as an optimized VQA ansatz may not perform effectively under a different noise environment. For the first time, we explore how to optimize VQA parameters to be robust against unknown shifted noise. We model the noise level as a random variable with an unknown probability density function (PDF), and we assume that the PDF may shift within an uncertainty set. This assumption guides us to formulate a distributionally robust optimization problem, with the goal of finding parameters that maintain effectiveness under shifted noise. We utilize a distributionally robust Bayesian optimization solver for our proposed formulation. This provides numerical evidence in both the quantum approximate optimization algorithm and the variational quantum eigensolver with hardware-efficient ansatz, indicating that we can identify parameters that perform more robustly under shifted noise. We regard this work as the first step toward improving the reliability of VQAs influenced by shifted noise from the parameter optimization perspective. |
| format | Article |
| id | doaj-art-1f70c6c338424805ae31b652f8ace178 |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-1f70c6c338424805ae31b652f8ace1782025-01-25T00:03:41ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511210.1109/TQE.2024.340930910547365Distributionally Robust Variational Quantum Algorithms With Shifted NoiseZichang He0https://orcid.org/0000-0002-1723-6568Bo Peng1Yuri Alexeev2https://orcid.org/0000-0001-5066-2254Zheng Zhang3https://orcid.org/0000-0002-2292-0030Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USAPhysical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USAComputational Science Division, Argonne National Laboratory, Lemont, IL, USADepartment of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USAGiven their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is that quantum noise is highly unstable and thus it is likely to shift in real time. This presents a critical problem as an optimized VQA ansatz may not perform effectively under a different noise environment. For the first time, we explore how to optimize VQA parameters to be robust against unknown shifted noise. We model the noise level as a random variable with an unknown probability density function (PDF), and we assume that the PDF may shift within an uncertainty set. This assumption guides us to formulate a distributionally robust optimization problem, with the goal of finding parameters that maintain effectiveness under shifted noise. We utilize a distributionally robust Bayesian optimization solver for our proposed formulation. This provides numerical evidence in both the quantum approximate optimization algorithm and the variational quantum eigensolver with hardware-efficient ansatz, indicating that we can identify parameters that perform more robustly under shifted noise. We regard this work as the first step toward improving the reliability of VQAs influenced by shifted noise from the parameter optimization perspective.https://ieeexplore.ieee.org/document/10547365/Bayesian optimization (BO)distributionally robust optimization (DRO)noise shiftvariational quantum algorithms (VQAs) |
| spellingShingle | Zichang He Bo Peng Yuri Alexeev Zheng Zhang Distributionally Robust Variational Quantum Algorithms With Shifted Noise IEEE Transactions on Quantum Engineering Bayesian optimization (BO) distributionally robust optimization (DRO) noise shift variational quantum algorithms (VQAs) |
| title | Distributionally Robust Variational Quantum Algorithms With Shifted Noise |
| title_full | Distributionally Robust Variational Quantum Algorithms With Shifted Noise |
| title_fullStr | Distributionally Robust Variational Quantum Algorithms With Shifted Noise |
| title_full_unstemmed | Distributionally Robust Variational Quantum Algorithms With Shifted Noise |
| title_short | Distributionally Robust Variational Quantum Algorithms With Shifted Noise |
| title_sort | distributionally robust variational quantum algorithms with shifted noise |
| topic | Bayesian optimization (BO) distributionally robust optimization (DRO) noise shift variational quantum algorithms (VQAs) |
| url | https://ieeexplore.ieee.org/document/10547365/ |
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